Pemanfaatan Metode Recursive Largest First Dalam Penyusunan Shift Kerja Karyawan Pada Rumah Sakit Royal Prima Medan
Bibliographic record
Abstract
Penysusunan jadwal karywan merupakan salah satu komponen penting di setiap perusahaan yang memiliki karyawan yang dalam jumlah benyak. Dimana pada Rumah Sakit Umum Roal Prima memiliki jumlah karyawan 600 orang dengan itulah karyawan yang bersangkutan mengalami kendalah dalam penyusunan jadwal karyawan. Berdasarkan data yang di dapatkan dari karyawan yang bersangkutan mengatakan penyususan penjadwalan kawayan dengan jaumlah 600 orang membutuhkan waktu 1 miggu. Pada penelitian menggunakan metode RLF dalam penysusunan penjadwawaln karyawan. Berdasarkan hasil yang di dapatkan dengan metode yang digunakan adalah pengerjaan penyusunan penjadwalan karyawan denga metode RLF lebih baik dari pada pekerjaan sebleummnya. Dimana pada metode yang sebelummnya harus menunggu waktu seminggu dalam pengerjaan penyusuanan penejadwalan karywan, dengan aplikasi memekau metode RLF ini dapat terselesaikan dalam 1 hari.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".